MATLAB and Simulink provide analytical, numerical, and applied tools for studying and designing various systems in mechanical and aerospace engineering. Their practical workflows for solving fundamental engineering problems and extensive adoption in the industry enable academics to translate fundamental engineering problem-solving skills into applied engineering solutions for complex systems.
Academic and Industry Examples
Enhanced Educational Platform
MATLAB and Simulink provide various educational workflows to facilitate teaching fundamental and advanced courses.
- MATLAB Live Editor provides a unified environment for explaining, coding, analyzing, reporting, and creating live scripts.
- Apps make explanations and analyses focused on specific systems and topics.
- MATLAB Online and MATLAB Mobile expand access and reach.
- MATLAB Grader provides automatic grading and feedback to students on a scale.
- MATLAB Copilot brings generative AI capabilities for code development and assistance in the MATLAB Desktop environment.
Teaching Fundamental Concepts
MATLAB and Simulink let educators focus on teaching fundamental topics and concepts in mechanical engineering. Symbolic Math Toolbox enables the presentation and derivation of theoretical descriptions of systems and phenomena, facilitating students' theoretical understanding beyond simple canonical systems. Students can study and analyze various systems by employing the extended capabilities of MATLAB for numerical analysis and Simulink for modeling and simulation.
Resources
Preparation and Practice for Engineered Systems
MATLAB, Simulink, and more than 100 add-on products provide a platform for analyzing complex real-world systems and designing products and services in various industries. Students’ familiarity and expertise directly translate to the needed industry qualification and experience.
Resources
- Aerospace and Defense
- Automotive
- Energy Production
- Industrial Automation and Machinery
- Extended Resources for Designing EVs, UAVs, and Renewable Energy Systems
Fundamental Methods for Physical Modeling
Mechanical engineers employ a comprehensive range of modeling approaches, considering factors such as reliance on fundamental physical principles, available system behavior data, and the required fidelity of the models. They use MATLAB and Simulink to develop models based on first principles, leveraging advanced solvers suitable for continuous, discrete, and event-driven systems. These products also offer extended capabilities to integrate models and components from multiple sources. Engineers can use data-driven methodologies for system identification, statistical analysis, and machine learning or deep learning applications.
Featured Offerings
Simscape for Mechanical Engineers
With Simscape, mechanical engineers can model systems based on the physical components and their physical network of connections. They can model multiphysics systems with blocks in domains, including mechanical (multibody, rotational, and translational), thermal and fluidics, electronic, mechatronic, and energy storage (battery). Simscape offers comprehensive libraries with prebuilt components at various fidelity levels. Engineers can add new components based on implicit equations defining their behavior. From 3D visualization of multibody systems to logging or plotting of principal and derived variables, Simscape facilitates better understanding and analysis of physical systems for mechanical engineers.
Featured Offerings
- Modeling Physical Systems with Simscape (57:25)
- Single and Double Mass-Spring-Damper in Simulink and Simscape
- Physical Modeling Examples
- Getting Started with Physical Modeling Using Simscape
- Simscape Results Explorer
Combining Physics and Data
MATLAB and Simulink offer a unified platform for combining data and physics to model complex physical systems. Engineers can perform parameter estimation to tune their system response with unknown parameters to match physical system behavior. They can build Reduced Order Models (ROM) to facilitate large amounts of data from experiments or high-fidelity simulations in system-level design, create virtual sensors, or speed up control and testing procedures. Using scientific machine learning (SciML) methods, such as physics-informed neural networks, engineers can embed physical laws into data-driven methods, improving model accuracy and generalization.
Featured Offerings
Comprehensive Engineering Design and Implementation
MATLAB and Simulink support various steps of engineering design and implementations of products and services through Model-Based Design and Model-Based Systems Engineering (MBSE). A digital thread unifies the design lifecycle. It encompasses capturing stakeholder requirements, designing system architecture, subsystems, and components. Additionally, it involves implementing hardware and software, as well as verifying and validating them through testing.
Featured Offerings
Optimize for Better Designs
MATLAB and Simulink offer an extensive set of functionalities for solving differential and algebraic systems, as well as classical and advanced optimization methods. Engineers can also use various tools for the design of experiments and parallel computing to facilitate the design of complex systems.
Featured Offerings
Connecting with Other Tools and Environments
Engineers can interface MATLAB with programming languages such as C/C++, Java®, Python®, and Fortran®. They can also bring MATLAB, C/C++, Fortran, and Python codes directly into Simulink. Co-simulation of Simulink and third-party tools is available through S-Function and FMU interfaces. Engineers can deploy MATLAB and Simulink applications to other environments as executable programs, apps, FMU, and software components.
Featured Offerings
Hardware Integration
In MATLAB and Simulink, automatic code generation ensures a reliable and repeatable transition from software modeling, simulation, and testing to hardware integration. Mechanical engineers can rapidly build prototypes with support for an extended range of microcontrollers, testing boards, programmable chips (FPGA, SoC, and ASIC), processing units (CPU, GPU), embedded systems, PLCs, and real-time target machines. They can transition smoothly from model-in-the-loop (MIL) to hardware-in-the-loop (HIL) and its variations for verification and validation.
To capture data from hardware, test and measurement toolboxes enable connection with measurement equipment and sensors. Multiple instrumentation protocols (I2C, SPI, and Bluetooth SPP) and industrial communication protocols (OPC UA, Modbus, and MQTT) are supported.
Featured Offerings
- Products for Code Generation
- Connect MATLAB and Simulink to Hardware
- Arduino® and Raspberry Pi® Support from MATLAB and Simulink
- What Is Hardware-in-the-Loop (HIL)?
